The therapeutic effect of wine-processed Corni Fructus on chronic renal failure in rats through the interference with the LPS/IL-1-mediated inhibition of RXR function.

ETHNOPHARMACOLOGICAL RELEVANCE: Corni Fructus, derived from the fruit of Cornus officinalis Sieb. et Zucc, is a widely utilized traditional Chinese medicine (TCM) with established efficacy in the treatment of diverse chronic kidney diseases. Crude Corni Fructus (CCF) and wine-processed Corni Fructus (WCF) are the main processed forms of Corni Fructus. Generally, TCM is often used after processing (paozhi). Despite the extensive use of processed TCM, the underlying mechanisms of processing for most TCMs have been unclear so far. AIM OF THE STUDY: In this study, an integrated strategy combined renal metabolomics with proteomics was established and investigated the potential processing mechanisms of CCF or WCF on chronic renal failure (CRF) models. MATERIALS AND METHODS: Firstly, the differences in biochemical parameters and pathological histology were compared to evaluate the effects of CCF and WCF on CRF model rats. Then, the tissue differential metabolites and proteins between CCF and WCF on CRF model rats were screened based on metabolomics and proteomics technology. Concurrently, a combined approach of metabolomics and proteomics was employed to investigate the underlying mechanisms associated with these marker metabolic products and proteins. RESULTS: Compared to the MG group, there were 27 distinct metabolites and 143 different proteins observed in the CCF-treatment group, while the WCF-treatment group exhibited 24 distinct metabolites and 379 different proteins. Further, the integration interactions analysis of the protein and lipid metabolite revealed that both WCF and CCF improved tryptophan degradation and LPS/IL-1-mediated inhibition of RXR function. WCF inhibited RXR function more than CCF via the modulation of LPS/IL-1 in the CRF model. Experimental results were validated by qRT-PCR and western blotting. Notably, the gene expression amount and protein levels of FMO3 and CYP2E1 among 8 genes influenced by WCF were higher compared to CCF. CONCLUSION: The results of this study provide a theoretical basis for further study of Corni Fructus with different processing techniques in CRF. The findings also offer guidance for investigating the mechanism of action of herbal medicines in diseases employing diverse processing techniques.

Improved analysis ZIC-HILIC-HCD-Orbitrap method for mapping the glycopeptide by mass spectrometry.

Glycosylation is one of the most common post-translational modifications (PTMs). Protein glycosylation analysis is the bottleneck to deeply understand their functions. At present, the LC-MS analysis of glycosylated post-translational modification is mainly focused on the analysis of glycopeptides. However, the factors affecting the identification of glycopeptides were not fully elucidated. In the paper, we have carefully studied the factors, e.g., HILIC materials, search engines, protein amount, gradient duration, extraction solution, etc. According to the results, HILIC materials were the most important factors affecting the glycopeptides identification, and the amphoteric sulfoalkyl betaine stationary phase enriched glycopeptides 6-fold more compared to the amphiphilic ion-bonded fully porous spherical silica stationary phase. We explored the influence of the extraction solutions on glycan identification. Comparing sodium dodecyl sulfate (SDS) and urea (UA), the results showed that N-glycolylneuraminic acid (NeuGc) type of glycan content was found to be increased 1.4-fold in the SDS compared to UA. Besides, we explored the influence of the search engine on glycopeptide identification. Comparing pGlyco3.0 and MSFragger-Glyco, it was observed that pGlyco3.0 outperformed MSFragger-Glyco in identifying glycopeptides. Then, using our optimized method we found that there was a significant difference in the distribution of monosaccharide types in plasma and brain tissue, e.g., the content of NeuAc in brain was 5-fold higher than that in plasma. To importantly, two glycoproteins (Neurexin-2 and SUN domain-containing protein 2) were also found for the first time by our method. In summary, we have comprehensively studied the factors influencing glycopeptide identification than any previous research, and the optimized method could be widely used for identifying the glycoproteins or glycolpeptides biomarkers for disease detection and therapeutic targets.

Optimized approach for active peptides identification in Cerebrolysin by nanoLC-MS.

Cerebrolysin (CBL) is a peptide-rich preparation made by hydrolysis and purified extraction of porcine brain. CBL contains various neuroprotective peptides, such as neurotrophic factor, nerve growth factor and ciliary neurotrophic factor, which can be used to treat neurodegenerative diseases. However, the active peptides in CBL had not been studied in depth. In this study, the following was carried out in order to investigate the active peptides in CBL. First, CBL samples were treated using organic reagents (acetonitrile and acetone) to precipitate the proteins and different solid phase extraction methods (MCX mixed-mode cartridges, C18 SPE cartridge columns and HILIC sorbent). Then the samples were analyzed using nanoLC-MS, followed by the identification of peptides using different sequence analysis software (PEAKS, pNovo and novor). Finally, bioinformatics analysis was performed to predict peptides with potential neuroprotective functions in CBL, such as anti-inflammatory and antioxidant peptides. Results showed that the number of peptides obtained by the MCX method coupled with PEAKS was the highest and the method was the most stable. Bioinformatic analysis of the detected peptides showed that two anti-inflammatory peptides (LLNLQPPPR and LSPSLRLP) and an antioxidant peptide (WPFPR) might be neuroprotective peptides in CBL. In addition, this study found that some peptides in CBL were present in myelin basic protein and tubulin beta chain. The results of this study for the detection of active peptides in CBL laid the foundation for the subsequent study of its active ingredients.

Classification of three types of ginseng samples based on ginsenoside profiles: appropriate data normalization improves the efficiency of multivariate analysis.

Background: It is well known that ginsenosides are the main active ingredients in ginseng, and they have also been important indexes for assessing the quality of ginseng. However, the absolute contents of ginsenosides in ginseng were shown to be varied with the origin, cultivated type, cultivated year and climate. It is a great challenge to distinguish the commercial types of ginsengs according to the content of one or several ginsenosides. Methods: The common commercial types of ginsengs are white ginseng (WG), red ginseng (RG), American ginseng (AG). To clearly illustrate the differences among WG, RG and AG at the ginsenosides level, we established a strategy for the detection and identification of ginsenosides based on an optimized LC-Q-Orbitrap MS/MS method coupled with an in-house database of ginsenosides. Before and after the normalization, the ginsenosides datasheet was analyzed and compared using several state-of-the-art multivariate statistical analysis methods. Results: Here, 81 ginsenosides were identified in different ginseng samples. The majority of the ginsenosides (59 in 81) were all shared by WG, RG and AG. When the shared ginsenosides datasheet was normalized by the level of ginsenoside Ro, our analysis strategy clearly divided the ginseng samples into three groups (i.e., WG, RG and AG groups). We found that the ginsenoside profiles in RG and WG were significantly different from those in AG. The potential markers and multivariate diagnostic models differentiating the three types of ginsengs were also indicated. Conclusion: Our novel methodology based on ginsenoside profiles is more robust than existing methods, and data normalization is required to improve the efficiency of multivariate statistical analysis.

Effects of polystyrene microplastics acute exposure in the liver of swordtail fish (Xiphophorus helleri) revealed by LC-MS metabolomics.

As global pollution, microplastics pollution has aroused growing concerns. In our experiment, the effect of microplastics acute exposure on the liver of swordtail fish was investigated by using LC-MS metabolomics. Fishes treated with high concentration polystyrene microspheres (1 µm) for 72 h were divided into three concentration groups: (A) no microplastics, (B): 1 × 106 microspheres L-1, (C): 1 × 107 microspheres L-1. Metabolomic analysis indicated that exposure to microplastics caused alterations of metabolic profiles in swordtail fish, including 37 differential metabolites were identified in B vs. A, screened out ten significant metabolites, which involved 14 metabolic pathways. One hundred three differential metabolites were identified in C vs. A, screened out 16 significant metabolites, which involved 30 metabolic pathways. Six significant metabolites were overlapping in group B vs. A and C vs. A; they are 3-hydroxyanthranilic acid, l-histidine, citrulline, linoleic acid, pantothenate, and xanthine. In addition, four metabolic pathways are overlapping in group B vs. A and C vs. A; they are beta-alanine metabolism, biosynthesis of amino acids, linoleic acid metabolism, and aminoacyl-tRNA biosynthesis. These differential metabolites were involved in oxidative stress, immune function, energy metabolism, sugar metabolism, lipid metabolism, molecule transport, and weakened feed utilization, growth performance, nutrient metabolism, and animal growth. Furthermore, we found that the number of interfered amino acids and microplastics showed a dose-effect. In summary, great attention should be paid to the potential impact of microplastics on aquatic organisms.